## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'steaks' and 'kale salads', which we can represent symbolically as $x_1$ and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to minimize is $3x_1 + 8x_2$.

## 3: Define the constraints in symbolic notation
The constraints given are:
1. $7x_1 + 2x_2 \geq 28$
2. $8x_1 + x_2 \geq 20$
3. $-9x_1 + 9x_2 \geq 0$
4. $7x_1 + 2x_2 \leq 58$
5. $8x_1 + x_2 \leq 27$

## 4: Consider the variable bounds
- $x_1$ (steaks) can be a non-integer.
- $x_2$ (kale salads) must be a whole number.

## 5: Represent the problem in the required JSON format
```json
{
    'sym_variables': [('x1', 'steaks'), ('x2', 'kale salads')],
    'objective_function': '3*x1 + 8*x2',
    'constraints': [
        '7*x1 + 2*x2 >= 28',
        '8*x1 + x2 >= 20',
        '-9*x1 + 9*x2 >= 0',
        '7*x1 + 2*x2 <= 58',
        '8*x1 + x2 <= 27'
    ]
}
```

## 6: Implement the problem using Gurobi
```python
import gurobi

def solve_optimization_problem():
    # Create a new model
    model = gurobi.Model()

    # Define the variables
    steaks = model.addVar(name="steaks", lb=0)  # Non-integer, non-negative
    kale_salads = model.addVar(name="kale_salads", lb=0, integrality=gurobi.GRB.INTEGER)  # Integer, non-negative

    # Define the objective function
    model.setObjective(3 * steaks + 8 * kale_salads, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(7 * steaks + 2 * kale_salads >= 28)
    model.addConstr(8 * steaks + kale_salads >= 20)
    model.addConstr(-9 * steaks + 9 * kale_salads >= 0)
    model.addConstr(7 * steaks + 2 * kale_salads <= 58)
    model.addConstr(8 * steaks + kale_salads <= 27)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Steaks: {steaks.varValue}")
        print(f"Kale Salads: {kale_salads.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```